Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+from diff_match_patch import diff_match_patch
+
+# Load grammar correction pipeline
+@st.cache_resource
+def load_grammar_model():
+    return pipeline("text2text-generation", model="vennify/t5-base-grammar-correction")
+
+# Optional: load explanation model (like flan-t5)
+@st.cache_resource
+def load_explainer_model():
+    return pipeline("text2text-generation", model="google/flan-t5-large")
+
+grammar_model = load_grammar_model()
+explainer_model = load_explainer_model()
+dmp = diff_match_patch()
+
+st.title("Grammarly-like AI Writing Assistant")
+st.markdown("Fix grammar, punctuation, spelling, tenses — with explanations and tips!")
+
+# User input
+user_input = st.text_area("Enter your sentence, paragraph, or essay:", height=200)
+
+if st.button("Correct Grammar"):
+    if user_input.strip():
+        # Correct the input
+        output = grammar_model(f"grammar: {user_input}", max_length=512, do_sample=False)[0]["generated_text"]
+        st.subheader("Corrected Text")
+        st.success(output)
+
+        # Show word-by-word diff
+        st.subheader("Changes Highlighted")
+        diffs = dmp.diff_main(user_input, output)
+        dmp.diff_cleanupSemantic(diffs)
+        html_diff = ""
+        for (op, data) in diffs:
+            if op == -1:
+                html_diff += f'<span style="background-color:#fbb;">{data}</span>'
+            elif op == 1:
+                html_diff += f'<span style="background-color:#bfb;">{data}</span>'
+            else:
+                html_diff += data
+        st.markdown(f"<div style='font-family:monospace;'>{html_diff}</div>", unsafe_allow_html=True)
+
+        # Explanation
+        if st.button("Explain Corrections"):
+            explanation_prompt = f"Explain the grammar issues in this text and how it was improved: {user_input}"
+            explanation = explainer_model(explanation_prompt, max_length=200)[0]['generated_text']
+            st.subheader("Explanation")
+            st.info(explanation)
+
+        # Suggest improvements
+        if st.button("Suggest Improvements"):
+            suggest_prompt = f"Suggest improvements to make this writing more professional: {output}"
+            suggestions = explainer_model(suggest_prompt, max_length=200)[0]['generated_text']
+            st.subheader("Suggestions")
+            st.warning(suggestions)